计及风电的发电商报价多智能体模型  

Multi-agent Based Bidding Strategy Model Considering Wind Power

作　　者：黄飞虎 李沛东[2] 彭舰 董石磊[1] 赵红磊 宋卫平 李强 HUANG Feihu;LI Peidong;PENG Jian;DONG Shilei;ZHAO Honglei;SONG Weiping;LI Qiang(Aostar Information Technologies Co.,Ltd.,Chengdu 610000,China;College of Computer Science,Sichuan University,Chengdu 610065,China;State Grid Information&Telecommunication Group Co.,Ltd.,Beijing 102211,China)

机构地区：[1]四川中电启明星信息技术有限公司,成都610000 [2]四川大学计算机学院,成都610065 [3]国网信息通信产业集团有限公司,北京102211

出　　处：《计算机科学》2024年第S01期1183-1190,共8页Computer Science

基　　金：四川省重点研发计划(2023YFG0112,22ZYFG0034);四川省重点实验室开放课题(SCITLAB-20001);四川大学博士后交叉学科基金(10822041A2137);四川大学宜宾市合作项目(2020CDYB-30)。

摘　　要：新型电力系统背景下,新能源发电商的报价问题一直是电力现货市场中的研究热点。相比传统能源,风电出力受外界不确定性因素的影响较大,给风力发电商求解最优报价带来了挑战。为此,基于多智能体强化学习算法WoLF-PHC构建了计及风电的发电商报价策略模型。模型中,考虑了风电、火电和水电3种能源参与的现货市场,每一个发电商抽象为一个智能体,且基于随机约束规划算法建模风电智能体的收益函数;对于智能体的报价策略模型,将D3QN与WoLF-PHC算法结合,使模型能够满足报价时智能体状态空间复杂的情况;此外,对于交互环境的建模,提出利用DDPM扩散模型生成风电出力数据,优化风电出清场景的仿真。最后,基于3节点的电力仿真系统开展模拟实验,实验结果表明,提出的风电收益函数建模、WoLF-PHC改进、风电出力生成等技术是可行的,能有效解决风电参与竞价的现货市场报价问题,并且能够在较少的迭代次数后学习到较优的策略。ed as an intelligent agent,and a stochastic constrained planning algorithm is used to model the profit function of the wind power agent.For the pricing strategy model of the agents,the D3QN algorithm is combined with the WoLF-PHC algorithm,which enables the model to handle complex state spaces when bidding.In addition,to model the interactive environment,a DDPM diffusion model is proposed to generate wind power output data and optimize the simulation of wind power clearing scenarios.In this paper,simulation experiments are carried out based on a 3-node power simulation system.Experimental results show that the proposed wind power profit function modeling,WoLF-PHC improvement,wind power output generation,and other techniques are feasible,which can effectively solve the bidding pricing problem of wind power in the spot market,and learn better strategy after fewer iterations.

关 键 词：WoLF-PHC 多智能体强化学习 电力现货市场 竞价策略 扩散模型 

分 类 号：TP389[自动化与计算机技术—计算机系统结构]